Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Pythonistaに憧れた分析屋の奮闘記
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
kanan
June 09, 2017
Programming
0
1.3k
Pythonistaに憧れた分析屋の奮闘記
kanan
June 09, 2017
Tweet
Share
More Decks by kanan
See All by kanan
Pythonデータ分析コトハジメinFukuoka
kanan
0
94
Pythonデータ分析コトハジメin静岡
kanan
0
110
Python超入門データ分析編-PyLadiesCaravan広島2nd-
kanan
0
110
PyLadiesCaravan_in_苫小牧
kanan
0
130
Python超入門_データ分析編in青森
kanan
0
200
Pythonデータ分析コトハジメin愛知3rd
kanan
1
150
PyLadiesCaravan in 大阪
kanan
0
290
PyLadiesCaravan in 名古屋Returns
kanan
0
200
PyLadiesCaravan in 愛媛(Python入門データ分析編)
kanan
0
350
Other Decks in Programming
See All in Programming
The Past, Present, and Future of Enterprise Java
ivargrimstad
0
510
文字コードの話
qnighy
44
17k
Agentic AI: Evolution oder Revolution
mobilelarson
PRO
0
160
Codex の「自走力」を高める
yorifuji
0
1.2k
オブザーバビリティ駆動開発って実際どうなの?
yohfee
3
820
The Past, Present, and Future of Enterprise Java
ivargrimstad
0
440
Claude Code Skill入門
mayahoney
0
230
コードレビューをしない選択 #でぃーぷらすトウキョウ
kajitack
3
890
AIとペアプロして処理時間を97%削減した話 #pyconshizu
kashewnuts
1
220
New in Go 1.26 Implementing go fix in product development
sunecosuri
0
420
Claude Code の Skill で複雑な既存仕様をすっきり整理しよう
yuichirokato
1
360
Go Conference mini in Sendai 2026 : Goに新機能を提案し実装されるまでのフロー徹底解説
yamatoya
0
560
Featured
See All Featured
Future Trends and Review - Lecture 12 - Web Technologies (1019888BNR)
signer
PRO
0
3.3k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
31
3.1k
Game over? The fight for quality and originality in the time of robots
wayneb77
1
130
Principles of Awesome APIs and How to Build Them.
keavy
128
17k
Easily Structure & Communicate Ideas using Wireframe
afnizarnur
194
17k
sira's awesome portfolio website redesign presentation
elsirapls
0
190
We Have a Design System, Now What?
morganepeng
55
8k
Lightning talk: Run Django tests with GitHub Actions
sabderemane
0
140
Exploring the relationship between traditional SERPs and Gen AI search
raygrieselhuber
PRO
2
3.7k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
122
21k
The #1 spot is gone: here's how to win anyway
tamaranovitovic
2
980
Design in an AI World
tapps
0
170
Transcript
PythonistaʹಌΕͨੳͷฃಆه @kanan* 2017.06.07 ΈΜͳͷPythonษڧձ#25
ࣗݾհ ˎKANANʢ͔ͳΜʣ ɹɹcompass-ID @kanan* ɹɹTwitter-ID @Addition_quince ˎ͓ࣄ ɹɹSIerاۀͰσʔλαΠΤϯεؔ࿈ۀΛͬͯ·͢ ɹɹݴޠSAS ˎPyLadiesʹ2016.ळ͔Βͪΐͪ͜ΐ͜ࢀՃ
ˎझຯԅɻ͓ञ͕େ͖ɻ
ࠓͷ͓ͳ͠ ˎϓϩάϥϛϯάʹڵຯ͕ͳ͔ͬͨੳ͕ ˎεΩϧΞοϓͨͯ͘͠PythonʹखΛग़ͦ͏ͱܾҙ͠ ˎ৺ંΕͳ͕Βฃಆͨ͠10ϲ݄ؒͷ͓Ͱ͢ PythonistaʹಌΕͨੳͷฃಆه Pythonista Lv0 → Lv1.5 ※͖ͬͱੌ͍ਓLv
20͘Β͍
Pythonͱͷग़ձ͍ σʔλੳܥɹˠɹPython Α͋͘Δͭ
͜Ε·Ͱͷϓϩάϥϛϯάྺ 2013 2014 2015 2016 2012 2011 େֶ γεςϜΤϯδχΞ σʔλαΠΤϯςΟετ
ˎੲϓϩάϥϛϯάͯͨͭ͠Γͩͬͨ ˎࣄͰϚωδϝϯτ͕ଟ͘ͳΓɺ৮Βͳ͍ۭനͷظؒ ˎSASݴޠͱ͍͍ͳ͕ΒπʔϧͬΆ͍ ϓϩάϥϛϯάɹʹɹࣄ
͜Ε·Ͱͷϓϩάϥϛϯάྺ ͪΌΜͱϓϩάϥϛϯάͬͯͳ͍
PythonσϏϡʔΛܾҙ PyLevel 0 20169݄ ˎҟ༷ͳ΄ͲͷΔؾ ˎΔੳͷࣝ
ͦͷ̍िؒޙɾɾɾ PyLevel 0 20169݄ ͦͷ̎ ˎΠϯετʔϧํ๏ݕࡧ͢Δͱ ɹɹΓํ͕ຯʹҧ͏ ˎHomebrewʁpyenvʁφχιϨ ˎPython3ΛೖΕͨͷʹ ɹɹόʔδϣϯ͕Python2ͬͯͳΔ
ˎ.bash_plofileͳ͍͠ʂ ˎPython͡ΊΔͨΊʹങͬͨ ɹɹMACͷ͍ํ͕Θ͔Βͳ͍ Πϯετʔϧ Ͱ͖ͳ͍ʂ
ܸɹ
ͦΜͳ࣌ɺPyLadiesTokyoͱग़ձ͏
PyLadies TokyoͰSTEP UP PyLadies Tokyo ळ߹॓ 2016 [2016.10.8-10] PyLadies Tokyo
Meet Up #16 [2016.11.27] PyLadies Tokyo Meet Up #17 [2016.12.11] PyLadies Tokyo Meet Up #20 [2016.03.25] *ڥߏங,AnacondaͱJupyterNotebookͷ͍ํΛֶͿ *ڝٕϓϩάϥϛϯάͰPythonͷॻ͖ํΛֶͿ *ϚΠίϯϘʔυ(STM32 Discovery)ʹMicro PythonͰLνΧ *WebεΫϨΠϐϯάΛͬͯΈΔ
ݸਓతʹσʔλੳपลͰษڧ ˎσʔλੳܥ ɹɹɹ1) titanicੜଘऀ༧ଌ ɹɹɹ2) ΞϝϦΧͷՈͷച٫Ձ֨༧ଌ ɹɹɹ3) ίϯϏχͷച্͛༧ଌ ˎͦͷଞ ɹɹɹ1)
खॻ͖ࣈͷೝࣝ ɹɹɹ2) RaspberryPi3ͰPythonͬͯΈΔ 2017.3 ʙ2017.4
PyLevel 1.0 20174݄ ˎΞϧΰϦζϜָ͍͠ ˎԿ͔Ͱ͖Δͱخ͍͠ ˎExcelSASͰ ɹͬͯͨࣄ͕PythonͰ ˎͬͱ৭ʑͬͯΈ͍ͨ Δؾ෮׆ɻͬͱೖऀϨϕϧʹ
νϟϨϯδ ͕ࣗڵຯ͕͋ΔςʔϚͰ Γ͍ͨͱࢥͬͨͷΛΖ͏
ΟεΩʔͰػցֶशʹઓ ʲͳΜͰΟεΩʔʁʳ ɹˎͱΓ͋͑ͣࢲ͕͖ ɹˎϫΠϯΈ͍ͨʹฑ͕Ռͯ͠ͳ͘ଟ͍Θ͚͡Όͳ͍ ɹˎւ֎ͷϏʔϧΈ͍ͨʹຯ߳Γͷಛ͕͖ͬΓͯ͠Δ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ͖ͳฑ͔ΒͨͿΜ͓ޱʹ߹͏ͷΛਪન
Ϩίϝϯυ
Anaconda Continuum Analyticsࣾఏڙͷ σʔλੳͰΑ͘ར༻͞ΕΔϥΠϒϥϦ܊Λ ҰׅΠϯετʔϧͰ͖ͪΌ͏ύοέʔδ ΟεΩʔͰػցֶशʹઓ Jupyter Notebook ϒϥβͰಈ࡞͢Δରܕ࣮ߦڥ ίʔυهड़ͱ࣮ߦɺίϝϯτૠೖ͕Ͱ͖ɺ
݁Ռͷอଘڞ༗ʹศར AnacondaೖΕΔͱσϑΥϧτͰೖͬͯΔ ɹPythonҎ֎ʹR, node.js, RubyෳݴޠʹରԠ ʲ࣮ߦڥʳ
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ̍ʣҰੜݒ໋σʔλΛूΊΔ ̎ʣskimageΛͬͯHOGಛྔΛऔಘ͢Δ ̏ʣֶशσʔλͱςετσʔλʹׂ ̐ʣsklearnΛͬͯCodeBookΛ࡞͠BoVWʹม ̑ʣֶशͱςετ ▪༻ͨ͠ϥΠϒϥϦ ɹɹskimage,
matplotlib, sklearn, glob, os
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ̍ʣҰੜݒ໋σʔλΛूΊΔ 899ຕ ࣗͷ͖ͳ11ฑ͚ͩͰ৺ંΕͯఘΊΔ
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ̎ʣskimageΛͬͯHOGಛྔΛऔಘ͢Δ HOG(Histograms of Oriented Gradients) ɹہॴྖҬ (ηϧ)
ͷًͷޯํΛώετάϥϜԽ 1.ը૾ΛదͳαΠζʹϦαΠζ͠ɺάϨΠεέʔϧͰಡΈࠐΉ 2.֤pixelͷً͔ΒޯڧͱޯํΛٻΊΔ 3.ηϧྖҬ͝ͱʹώετάϥϜΛٻΊΔʢࠓճ8×8ϐΫηϧʣ 4.ϒϩοΫ͝ͱʹਖ਼نԽ͠ɺಛྔΛநग़͢Δʢࠓճ3×3ηϧʣ
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ̎ʣskimageΛͬͯHOGಛྔΛऔಘ͢Δ
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ̐ʣsklearnΛͬͯCodeBookΛ࡞͠BoVWʹม BoVW(Bag-of-Visual-Words)ܗࣜ ɹը૾ͷಛྔΛϕΫτϧԽ͠ώετάϥϜʹͨ͠ͷ ɾɾɾɾ ɾɾɾ ɾɾɾ Visual-word
vectors Codebook (දύλʔϯͷϦετʣ ç ç ç ç
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ̑ʣֶशͱςετ ը૾σʔλͱਖ਼ղͷϥϕϧΛֶशͤ͞Δɹ※SVMʢαϙʔτϕΫλϚγϯʣΛ࠾༻ άϦουαʔνͰϋΠύʔύϥϝʔλνϡʔχϯά
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ਖ਼ ɹ72% ςετ݁Ռ
ΟεΩʔͰػցֶशʹઓ Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ ͬͯΈͨ݁Ռ ▪ը૾ͷલॲཧ෦ ɹɹɾࠓճϥϕϧΞοϓͷը૾͔ΓΛ༻ͨ͠ͷͰɺ ɹɹɹҾ͖ͷࣸਅʹରԠͰ͖ͳ͍ʢͬͯΈͨΒյ໓తʣ ɹɹɾഎܠͱ͔ະߟྀͷ·· ɹɹɾOpenCVʁͳʹͦΕ ▪ֶश෦
ɹɹɾಛྔϕʔε(BoVW)ͰͷֶशΛ࠾༻͚ͨ͠Ͳɺ ɹɹɹσΟʔϓϥʔχϯά͏ͱͬͱਫ਼͕͋Δͷ͔ͳ
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̍ʣWebαΠτ͔Β֤ฑͷઆ໌ίϝϯτΛεΫϨΠϐϯά ̎ʣmecabͰ୯ޠʹׂ͠ɺܗ༰ࢺͱ෭ࢺ͚ͩऔΓग़͢ ̏ʣग़ݱͨ͠୯ޠΛ·ͱΊɺTFIDFܭࢉͰಛతͳޠΛಛఆ ̐ʣ୯ޠΛͬͯࣅ͍ͯΔฑΛΫϥελϦϯά
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̍ʣWebαΠτ͔Β֤ฑͷઆ໌ίϝϯτΛεΫϨΠϐϯά beautiful soupΛ༻ ɹɹᶃHTMLऔಘ ɹɹᶄ΄͍͠ใͷ෦ͷλάΛݟ͚ͭΔ ɹɹᶅߏղੳͯ͠ಛఆ෦Λநग़ ɹɹᶆσʔλϑϨʔϜʹม͢Δ
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̍ʣWebαΠτ͔Β֤ฑͷઆ໌ίϝϯτΛεΫϨΠϐϯά beautiful soupΛ༻ ɹɹᶃHTMLऔಘ ɹɹᶄ΄͍͠ใͷ෦ͷλάΛݟ͚ͭΔ ɹɹᶅߏղੳͯ͠ಛఆ෦Λநग़ ɹɹᶆσʔλϑϨʔϜʹม͢Δ
্ख͍͔ͣ͘ ࣌ؒΛ࿘අ
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̍ʣWebαΠτ͔Β֤ฑͷઆ໌ίϝϯτΛεΫϨΠϐϯά
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̍ʣWebαΠτ͔Β֤ฑͷઆ໌ίϝϯτΛεΫϨΠϐϯά
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̍ʣWebαΠτ͔Β֤ฑͷઆ໌ίϝϯτΛεΫϨΠϐϯά ฑͷղઆจ ޱίϛจ ͳΜͱ͔εΫϨΠϐϯάྃ ख࡞ۀͰCSV࡞
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̎ʣmecabͰ୯ޠʹׂ͠ɺܗ༰ࢺͱ෭ࢺ͚ͩऔΓग़͢ ߳Γڧ͍Ͱ͕͢ɺຯ͖ͬ͢Γ͍ͯͯ͠ɺඒຯ͍͠Ͱ͢ɻ ߳Γ//ڧ͍/Ͱ͢/͕/ɺ/ຯ//͖ͬ͢Γ/ͯ͠/͍ͯ/ɺ /ඒຯ͍͠/Ͱ͢/ɻ
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̏ʣग़ݱͨ͠୯ޠΛ·ͱΊɺTFIDFܭࢉͰಛతͳޠΛಛఆ BoW(Bag-of-Words) ܗࣜʹม จॻ ߦྻԽ TFIDF ܭࢉ
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̏ʣग़ݱͨ͠ܗ༰ࢺΛ·ͱΊɺTFIDFܭࢉͰಛతͳޠΛಛఆ TFIDF=TF×IDF TF→Ұ࿈ͷจॻͷͦͷ୯ޠͷग़ݱස IDF→ʢͦͷ୯ޠ͕͋Δจॻͷʗશจॻʣͷٯ ʲͦͷจॻΒ͠͞ʳ ɹͦͷจॻͰଟ͘Ͱͯ͘ΔͷʹɺଞͷจॻͰ͋·ΓͰͯ͜ͳ͍୯ޠ BoW(Bag-of-Words)ܗࣜʹม
จॻߦྻԽ TFIDFܭࢉ BoWܗࣜ=จॻΛͱͯ͠ѻ͏ʢϕΫτϧԽ͢Δʣ ※ࠓճTFʢ୯ޠͷग़ݱසʣΛ༻
WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ΟεΩʔͰػցֶशʹઓ ̐ʣ୯ޠΛͬͯࣅ͍ͯΔฑΛΫϥελϦϯά ֤ฑͷಛతͳ୯ޠTOP5Λͬͯ5ͭʹΫϥελϦϯά …͠Α͏ͱࢥͬͨΒ ˎ൱ఆʮͳ͍ʯͷߟྀ ˎআ֎ͨ͠΄͕͍͍୯ޠ ɹɹɹɹɹɹɹɹɹ ߟྀෆͰɺ
ͳ͍ΫϥελϦϯάʹ
͖ͳฑ͔ΒͨͿΜ͓ޱʹ߹͏ͷΛਪન Ϩίϝϯυ ΟεΩʔͰػցֶशʹઓ ຊ·Ͱʹ౸ୡͰ͖ͣ…ɻ ڧௐϑΟϧλϦϯάͱ͔ͰΓ͔͚ͨͬͨͲɺ ·ͣσʔλΛूΊΔͱ͍͏࠷େͷน͕ͬͯΔɻ
ΟεΩʔͰػցֶशʹઓͷཱྀଓ͘… Ϙτϧͷϥϕϧ͔ΒฑΛผ ը૾ೝࣝ WEBͷใ͔Β֤ฑͷಛΛநग़ ςΩετղੳ ͖ͳฑ͔ΒͨͿΜ͓ޱʹ߹͏ͷΛਪન Ϩίϝϯυ ˎਫ਼্ʂ90%ਖ਼ղ͍ͨ͠ɻOpenCVͬͯΈ͍ͨ ˎσʔλ૿ྔͱ൱ఆޠআ֎બఆɺϊΠζͷѻ͍ͷߟྀ ˎ͜Ε͔ΒؤுΔʂϢʔβෳධՁͰڧௐϑΟϧλϦϯά
WebΞϓϦέʔγϣϯ
·ͱΊ ˎPytonistaʹಌΕͨҰհͷੳͨͿΜPyLv1.5͘Β͍ʹͳΕͨ ˎ࠷ॳͷӈࠨΘ͔Βͳ͍࣌ɺؒͱҰॹ͕͍͍ ˎࣗͷڵຯͱֻ͚߹ΘͤΔͷͬͯؤுΓ͕࣋ଓ͢Δ ˎσʔλूΊΔͷେมͬͯᷚຊͩͬͨ ˎ·͡ͰwebΞϓϦέʔγϣϯΘ͔Γ·ͤΜ ɹಘҙͳਓɺͪΐͬͱͬͯΔਓɺ͓༑ୡʹͳ͍ͬͯͩ͘͞
͋Γ͕ͱ͏͍͟͝·ͨ͠ɻ